ArticleScientific reports2024
Innovative graph neural network approach for predicting soil heavy metal pollution in the Pearl River Basin, China.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Applications, Challenges, and Prospects of Artificial Intelligence in Crop Production.Plants (Basel, Switzerland) · 2026Review
- Unified artificial intelligence framework for modeling pollution dynamics and sustainable remediation in environmental chemistry.Scientific reports · 2025Article
- Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study.Scientific reports · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Predicting soil heavy metal (HM) content is crucial for monitoring soil quality and ensuring ecological health. However, existing methods often neglect the spatial dependency of data. To address this gap, our study introduces a novel graph neural network (GNN) model, Multi-Scale Attention-based Graph Neural Network for Heavy Metal Prediction (MSA-GNN-HMP). The model integrates multi-scale graph convolutional network (MS-GCN) and attention-based GNN (AGNN) to capture spatial relationships. Using surface soil samples from the Pearl River Basin, we evaluate the MSA-GNN-HMP model against four other models. The experimental results show that the MSA-GNN-HMP model has the best predictive performance for Cd and Pb, with a coefficient of determination (R
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